code: def modelGaussian():
      """Return data, parameters and Model class for Gaussian + Linear model."""
      # generate data with random noise added
      np.random.seed(7)
      x = np.linspace(0, 20, 401)
      y = gaussian(x, amplitude=24.56, center=7.6543, sigma=1.23)
      y -= 0.20 * x + 3.333 + np.random.normal(scale=0.23, size=len(x))
  
      mod = GaussianModel(prefix="peak_") + LinearModel(prefix="bkg_")
  
      # make parameters and set bounds
      pars = mod.make_params(
          peak_amplitude=21.0,
          peak_center=7.0,
          peak_sigma=2.0,
          bkg_intercept=2,
          bkg_slope=0.0,
      )
  
      pars["bkg_intercept"].set(min=0, max=10, brute_step=5.0)
      pars["bkg_slope"].set(min=-5, max=5, brute_step=5.0)
      pars["peak_amplitude"].set(min=20, max=25, brute_step=2.5)
      pars["peak_center"].set(min=5, max=10, brute_step=2.5)
      pars["peak_sigma"].set(min=0.5, max=2, brute_step=0.5)
  
      return x, y, mod, pars
  
file_path: lmfit-py/tests/test_max_nfev.py
imports: import numpy
  import pytest
  from lmfit.lineshapes import gaussian
  from lmfit.minimizer import Minimizer
  from lmfit.models import GaussianModel, LinearModel
label_desc: Use image node_id 1 for calling a global function with example usage: modelGaussian() and returns: x,  y,  mod,  pars